Data preparation.
The labels of our training are generated by using the off-the-shelf unsupervised optical flow model [1]} on datasets Got10k [2]}, LaSOT [3]}, VID [4]} and YoutubeVOS [5]}, the data sampling strategy is similar to USOT [6]}. One reliable template frame and three search region frames with large temporal gaps are sampled from a video for cycle training.
The template patch is cropped as size \(127 \times 127\) , and the search frames are replicated as palindromes for tracking back to the initial frame. The spatial size of all input frames is resized as \(640 \times 480\)  without crop.
Specifically, our framework only requires objects' initial box and subsequent center position, reducing the reliance on pseudo-labels.
As the optical flow method struggles with scale variation of target objects, in the cycle training, the search frames are cropped as \(255 \times 255\)  according to the input object center and last frame estimated object scale instead of the scale initialized in pseudo-labels.
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